Autonomous Robotics Self-Localization Using Genetic Algorithms

In this work, a new approach for robotics self-location using constrained genetic algorithms is proposed. The model uses a location estimation stage based on Kalman filters so as to redefine the search space and finds the most accurate current position of a robot. Experiments show the promise of the method to predict for robotic applications.